Physics-informed learning of governing equations from scarce data
2022-04-01 13:00 - 14:00 (Asia/Seoul) | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Physics-informed learning of governing equations from scarce data”, Chen et al., Nature Communications, 2021
Abstract: Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.

대학원생 길라잡이 (Tips for graduate students)
2022-04-05 17:00 - 18:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
4월 5일 오후 5시, <대학원생 때 알았더라면 좋았을 것들>의 저자이신 권창현 교수님께서 대학원생 길라잡이라는 주제로 워크샵을 진행해주실 예정입니다.

줄리아 언어 프로그래밍 튜토리얼 (Julia programming tutorial)
2022-04-05 20:00 - 21:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
<경영과학을 위한 줄리아 언어 프로그래밍>의 저자이신 권창현 교수님께서 4월 5일 오후 8시 줄리아 프로그래밍 튜토리얼을 진행합니다. 본 튜토리얼은 한국어로 진행됩니다.

Universal biology in adaptation and evolution: dimensional reduction, and fluctuation-response relationship
2022-04-07 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: A macroscopic theory for cellular states with steady-growth is presented, based on consistency between cellular growth and molecular replication, together with robustness of phenotypes against perturbations. Adaptive changes in high-dimensional phenotypes are shown to be restricted within a low-dimensional slow manifold, from which a macroscopic law for cellular states is derived, as is confirmed by adaptation experiments of bacteria under stress. The theory is extended to phenotypic evolution, leading to proportionality between phenotypic responses against genetic evolution and by environmental adaptation, which explains the evolutionary fluctuation-response relationship previously uncovered.
References

Kaneko K., Life: An Introduction to Complex Systems Biology, Springer (2006)
K. Kaneko, C.Furusawa, T. Yomo, “Macroscopic phenomenology for cells in steady-growth state”, Phys.Rev.X(2015) 011014
C. Furusawa, K. Kaneko “Global Relationships in Fluctuation and Response in Adaptive Evolution”, J of Royal Society Interface 12(2015), 20150482.
C. Furusawa, K. Kaneko ” Formation of Dominant Mode by Evolution in Biological Systems” Phys. Rev. E 97(2018)042410
K. Kaneko, C. Furusawa “Macroscopic Theory for Evolving Biological Systems Akin to Thermodynamics”, Annual Rev. Biophys. (2018) 47, 273-290
A. Sakata and K. Kaneko, “Dimensional Reduction in Evolving Spin-Glass Model: Correlation of Phenotypic Responses to Environmental and Mutational Changes”, Phys. Rev. Lett. (2020) 124, 218101
Q-Y. Tang and K. Kaneko, “ Dynamics-evolution correspondence in protein structures”, Phys. Rev. Lett. (2021) 127, 098103

RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy
2022-04-08 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy”, Behrendt et al., SoftwareX, 2019
Abstract: This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Rényi transfer entropy using the R package RTransferEntropy. We discuss the methodology, the bias correction applied to calculate effective transfer entropy and outline how to conduct statistical inference. Furthermore, we describe the package in detail and demonstrate its functionality by means of several simulated processes and present an application to financial time series.

An overview of methods used for multi-scale modeling and analysis
2022-04-14 10:30 - 11:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: TBA

A systems biology approach using multi-scale modeling to understand the immune response to tuberculosis infection and treatment
2022-04-14 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: Tuberculosis (TB) is one of the world’s deadliest infectious diseases. Caused by the pathogen Mycobacterium tuberculosis (Mtb), the standard regimen for treating TB consists of treatment with multiple antibiotics for at least six months. There are a number of complicating factors that contribute to the need for this long treatment duration and increase the risk of treatment failure. The structure of granulomas, lesions forming in lungs in response to Mtb infection, create heterogeneous antibiotic distributions that limit antibiotic exposure to Mtb. We can use a systems biology approach pairing experimental data from non-human primates with computational modeling to represent and predict how factors impact antibiotic regimen efficacy and granuloma bacterial sterilization. We utilize an agent-based, computational model that simulates granuloma formation, function and treatment, called GranSim. A goal in improving antibiotic treatment for TB is to find regimens that can shorten the time it takes to sterilize granulomas while minimizing the amount of antibiotic required. We also created a whole host model, called HOSTSIM, to study Mtb dynamics within a human host. Overall, we use these models to help better understand TB treatment and strengthen our ability to predict regimens that can improve clinical treatment of TB.

A topological data analysis based classifier
2022-04-15 11:00 - 13:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “A topological data analysis based classifier”, Kindelan et al., arXiv, 2022
Abstract: Topological Data Analysis is an emergent field that aims to discover the underlying dataset’s topological information. Topological Data Analysis tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes a different Topological Data Analysis pipeline to classify balanced and imbalanced multi-class datasets without additional ML methods. Our proposed method was designed to solve multi-class problems. It resolves multi-class imbalanced classification problems with no data resampling preprocessing stage. The proposed Topological Data Analysis-based classifier builds a filtered simplicial complex on the dataset representing high-order data relationships. Following the assumption that a meaningful sub-complex exists in the filtration that approximates the data topology, we apply Persistent Homology to guide the selection of that sub-complex by considering detected topological features. We use each unlabeled point’s link and star operators to provide different sized and multi-dimensional neighborhoods to propagate labels from labeled to unlabeled points. The labeling function depends on the filtration entire history of the filtered simplicial complex and is encoded within the persistent diagrams at various dimensions. We select eight datasets with different dimensions, degrees of class overlap, and imbalanced samples per class. The TDABC outperforms all baseline methods classifying multi-class imbalanced data with high imbalanced ratios and data with overlapped classes. Also, on average, the proposed method was better than KNN and weighted-KNN and behaved competitively with SVM and Random Forest baseline classifiers in balanced datasets.

Dynamical and topological hallmarks of regulatory networks driving phenotypic plasticity and heterogeneity in cancers
2022-04-21 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract:
Metastasis and therapy resistance cause over 90% of cancer-related deaths. Despite extensive ongoing efforts, no unique genetic or mutational signature has emerged for metastasis. Instead, the ability of genetically identical cells to adapt reversibly by exhibiting multiple phenotypes (phenotypic/non-genetic heterogeneity) and switch among them (phenotypic plasticity) is proposed as a hallmark of metastasis. Also, drug resistance can emerge from such non-genetic adaptive cellular changes. However, the origins of such non-genetic heterogeneity in most cancers are poorly understood. I will present our findings on a) how non-genetic heterogeneity emerges in a population of cancer, and b) what design principles underlie regulatory networks enabling non-genetic heterogeneity across multiple cancers. Our results unravel how systems-levels approaches integrating mechanistic mathematical modeling with in vitro and in vivo data can identify causes and consequences of such non-genetic heterogeneity.

An Efficient Characterization of Complex-Balanced, Detailed-Balanced, and Weakly Reversible Systems
2022-04-22 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “An Efficient Characterization of Complex-Balanced, Detailed-Balanced, and Weakly Reversible Systems”, Craciun et al., SIAM Journal on Applied Mathematics, 2020
Abstract: Very often, models in biology, chemistry, physics, and engineering are systems of polynomial or power-law ordinary differential equations, arising from a reaction network. Such dynamical systems can be generated by many different reaction networks. On the other hand, networks with special properties (such as reversibility or weak reversibility) are known or conjectured to give rise to dynamical systems that have special properties: existence of positive steady states, persistence, permanence, and (for well-chosen parameters) complex balancing or detailed balancing. These last two are related to thermodynamic equilibrium, and therefore the positive steady states are unique and stable. We describe a computationally efficient characterization of polynomial or power-law dynamical systems that can be obtained as complex-balanced, detailed-balanced, weakly reversible, and reversible mass-action systems.

Scaling behaviors in physiological fluctuations: relevance to circadian regulation and insights into the development of Alzheimer’s disease
2022-04-28 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: Outputs from health biological systems display complex fluctuations that are not random but display robust and often self-similar (fractal) temporal correlations at different time scales— scaling behaviors. The scaling behaviors in the fluctuations of biological outputs such as neural activities, cardiac dynamics, motor activity are believed to be originated from feedbacks within the complex biological networks, reflecting the system adaptability to internal and external inputs. Supporting this concept, our studies have demonstrated a mechanistic link between the scaling regulation of physiological fluctuations and the circadian control system— a result of evolutionary adaptation to daily environmental light-dark cycles on the earth. In this talk, I will discuss certain evidence for this ‘scaling-circadian’ link and its related implications. Moreover, I will review some recent studies, in which we examined how the scaling patterns of human motor activity fluctuations change with aging and in Alzheimer’s disease. Our results showed that (1) alterations in scaling activity patterns occur before the clinical manifestation of Alzheimer’s disease (i.e., cognitive impairment) and predict cognitive decline and the risk for Alzheimer’s dementia; and (2) the progression of Alzheimer’s disease accelerates the aging effect on the scaling activity patterns. Our work provides strong evidence that altered scaling activity patterns may also be a risk factor for neurodegeneration, playing a role in the development and progression of Alzheimer’s disease.

Toroidal topology of population activity in grid cells
2022-04-29 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Toroidal topology of population activity in grid cells”, Gardner et al., Nature, 2021.
Abstract: The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations, and are organized in modules that collectively form a population code for the animal’s allocentric position. The invariance of the correlation structure of this population code across environments and behavioral states, independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to the positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.

The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes
2022-05-06 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes”, Katori et al., PNAS, 2022.
Abstract: Human sleep phenotypes can be defined and diversified by both genetic and environmental factors. However, some sleep phenotypes are difficult to evaluate without long-term, precise sleep monitoring, for which simple yet accurate sleep measurement is required. To solve this problem, we recently developed a state-of-the-art sleep/wake classification algorithm based on wristband-type accelerometers, termed ACCEL (acceleration-based classification and estimation of long-term sleep-wake cycles). In this study, we optimized and applied ACCEL to large-scale analysis of human sleep phenotypes. The clustering of an about 100,000-arm acceleration dataset in the UK Biobank using uniform manifold approximation and projection (UMAP) dimension reduction and density-based spatial clustering of applications with noise (DBSCAN) clustering methods identified 16 sleep phenotypes, including those related to social jet lag, chronotypes (“morning/night person”), and seven different insomnia-like phenotypes. Considering the complex relationship between sleep disorders and other psychiatric disorders, these unbiased and comprehensive analyses of sleep phenotypes in humans will not only contribute to the advancement of biomedical research on genetic and environmental factors underlying human sleep patterns but also, allow for the development of better digital biomarkers for psychiatric disorders.

Introduction to balanced networks
2022-05-12 10:30 - 11:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: The idea of balance between excitation and inhibition is central in the theory of biological neural networks. I will give a brief introduction to the concept of such balance, and an overview of the mathematical ideas that can be used to study it.

Plasticity and balance in neuronal networks
2022-05-12 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: I will first describe how to extend the theory of balanced networks to account for synaptic plasticity. This theory can be used to show when a plastic network will maintain balance, and when it will be driven into an unbalanced state. I will next discuss how this approach provides evidence for a novel form of rapid compensatory inhibitory plasticity. Experimental evidence for such plasticity comes from optogenetic activation of excitatory neurons in primate visual cortex (area V1) which induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state, but not during rest. I will shift gears in the final part of the talk, and discuss how community detection algorithms can help uncover the large scale organization of neuronal networks from connectome data.

Optimizing Oscillators for Specific Tasks Predicts Preferred Biochemical Implementations
2022-05-12 15:00 - 16:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Optimizing Oscillators for Specific Tasks Predicts Preferred Biochemical Implementations”, Agrahar and Rust., bioRxiv, 2022.
Abstract: Oscillatory processes are used throughout cell biology to control time-varying physiology including the cell cycle, circadian rhythms, and developmental patterning. It has long been understood that free-running oscillations require feedback loops where the activity of one component depends on the concentration of another. Oscillator motifs have been classified by the positive or negative net logic of these loops. However, each feedback loop can be implemented by regulation of either the production step or the removal step. These possibilities are not equivalent because of the underlying structure of biochemical kinetics. By computationally searching over these possibilities, we find that certain molecular implementations are much more likely to produce stable oscillations. These preferred molecular implementations are found in many natural systems, but not typically in artificial oscillators, suggesting a design principle for future synthetic biology. Finally, we develop an approach to oscillator function across different reaction networks by evaluating the biosynthetic cost needed to achieve a given phase coherence. This analysis predicts that phase drift is most efficiently suppressed by delayed negative feedback lo op architectures that operate without positive feedback.

Stochastic modelling of reaction-diffusion processes
2022-05-25 16:30 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: I will introduce mathematical and computational methods for spatio-temporal modelling in molecular and cell biology, including all-atom and coarse-grained molecular dynamics (MD), Brownian dynamics (BD), stochastic reaction-diffusion models and macroscopic mean-field equations. Microscopic (BD, MD) models are based on the simulation of trajectories of individual molecules and their localized interactions (for example, reactions). Mesoscopic (lattice-based) stochastic reaction-diffusion approaches divide the computational domain into a finite number of compartments and simulate the time evolution of the numbers of molecules in each compartment, while macroscopic models are often written in terms of mean-field reaction-diffusion partial differential equations for spatially varying concentrations.

Multi-resolution methods for modelling intracellular processes
2022-05-25 17:00 - 18:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: I will discuss the development, analysis and applications of multi-resolution methods for spatio-temporal modelling of intracellular processes, which use (detailed) Brownian dynamics or molecular dynamics simulations in localized regions of particular interest (in which accuracy and microscopic details are important) and a (less-detailed) coarser model in other regions in which accuracy may be traded for simulation efficiency. I will discuss the error analysis and convergence properties of the developed multi-resolution methods, their software implementation and applications of these multiscale methodologies to modelling of intracellular calcium dynamics, actin dynamics and DNA dynamics. I will also discuss the development of multiscale methods which couple molecular dynamics and coarser stochastic models in the same dynamic simulation.

KSIAM 2022 Spring Conference
2022-05-27 08:00 - 2022-05-29 17:00 | IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
KSIAM 2022 Spring Conference
Date
May 27, 2022 (Friday) – May 29, 2022 (Sunday)
Venue
Daejeon, IBS
Program
Detailed information will be updated.
The conference will be a joint meeting of the following 6 divisions:

Combinatorics
Data Science & Machine Learning
Imaging Science
Industrial Mathematics
Inverse Problems
Medical Mathematics

From live cell imaging to moment-based variational inference
2022-06-01 17:00 - 18:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
This talk will be presented online. Zoom link: 997 8258 4700 (pw: [REDACTED]
Abstract: Quantitative characterization of biomolecular networks is important for the analysis and design of network functionality. Reliable models of such networks need to account for intrinsic and extrinsic noise present in the cellular environment. Stochastic kinetic models provide a principled framework for developing quantitatively predictive tools in this scenario. Calibration of such models requires an experimental setup capable of monitoring a large number of individual cells over time, automatic extraction of fluorescence levels for each cell and a scalable inference approach. In the first part of the talk we will cover our microfluidic setup and a deep-learning based approach to cell segmentation and data extraction. The second part will introduce moment-based variational inference as a scalable framework for approximate inference of kinetic models based on single cell data.

Introduction to matrix and tensor factorization models and related stochastic nonconvex and constrained optimization algorithms
2022-06-02 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract. Matrix/tensor factorization models such as principal component analysis , nonnegative matrix factorization, and CANDECOM/PARAFAC tensor decomposition provide powerful framework for dimension reduction and interpretable feature extraction, which are important in analyzing high-dimensional data that comes in large volume. Their diverse applications include image denoising and reconstruction, dictionary learning, topic modeling, and network data analysis. Fitting such factorization models to training data gives rise to various nonconvex and constrained optimization algorithms. Moreover, such models can be trained efficiently for streaming data using stochastic/online versions of such algorithms. After introducing matrix/tensor factorization models and their applications in various contexts, we survey some well-known nonconvex constrained optimization algorithms such as block coordinate descent and projected gradient descent. We also discuss some recent developments in general stochastic optimization algorithms such as stochastic proximal gradient descent and stochastic regularized majorization-minimization and their convergence and complexity guarantees under general Markovian streaming data.

Approximating Solutions of the Chemical Master Equation using Neural Networks
2022-06-03 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Approximating Solutions of the Chemical Master Equation using Neural Networks”, Sukys et al., bioRxiv, 2022
Abstract: The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved analytically for most systems of practical interest. While Monte Carlo methods provide a principled means to probe the system dy- namics, their high computational cost can render the estimation of molecule number distributions and other numerical tasks infeasible due to the large number of repeated simulations typically required. In this paper we aim to leverage the representational power of neural networks to approximate the solutions of the CME and propose a framework for Neural Estimation of Stochastic Simulations for Inference and Exploration (Nessie). Our approach is based on training a neural network to learn the distributions predicted by the CME from a relatively small number of stochastic simulations, thereby accelerating computationally intensive tasks such as parameter exploration and inference. We show on biologically relevant examples that simple neural networks with one hidden layer are able to cap- ture highly complex distributions across parameter space. We provide a detailed discussion of the neural network implementation and code for easy reproducibility.

[사전 등록 필요] 논문 글쓰기 워크샵 1/2 - 논문 작성 원리: ABC 논문 작성법 (원병묵 교수님)
2022-06-03 16:00 - 17:00 | Conference room, (B109), Daejeon, Daejeon, 34126, Korea, Republic of
2022년 6월 3일 오후 4시 – 5시, 성균관대 신소재공학부의 원병묵 교수님의 논문 글쓰기 워크샵 강의가 있습니다. 참가를 원하시는 분은 구글 폼(링크)에서 사전 등록을 해주시기 바랍니다.
[요약]
논문은 저자와 독자 사이의 학문적 소통을 위한 논리적인 글이다. 이번 강연을 통해 논문을 작성하기 위한 기본 원리를 배울 수 있다. 특히, 연구가 거의 마무리되는 시점에 (After completing your research), 연구 결과를 그림과 표로 잘 정리한 다음에 (Based on well-organized figures and tables), 본격적으로 논문 작성을 시작하는 (Compose your manuscript from a title to a conclusion) ‘ABC 논문 작성법’을 소개한다. 논문 작성의 준비 과정으로 (A와 B의 과정), 연구 노트 작성 방법, 저널 클럽 운영 방법, 한 페이지 활용 방법을 설명한다. 논문 작성 준비가 완료되면, 제목부터 결론까지 순서대 로 논문 원고를 작성할 수 있다 (C의 과정). 이렇게 하면, 단기간에 집중하여 효율적으 로 논문을 작성할 수 있다.
*참고: 원병묵 교수의 과학 논문 쓰는 법

[사전 등록 필요] 논문 글쓰기 워크샵 2/2 – 영어 논문 쓰기: 두려움을 성취로 바꾸는 길 (박영민 연구원님)
2022-06-03 17:00 - 18:00 | Conference room, (B109), Daejeon, Daejeon, 34126, Korea, Republic of
2022년 6월 3일 오후 5시 – 6시, 부산교육정책연구소 박영민 연구원님의 논문 글쓰기 워크샵 강의가 있습니다. 참가를 원하시는 분은 구글 폼(링크)에서 사전 등록을 해주시기 바랍니다.
[요약] 이 ‘영어 논문 쓰기’ 워크숍은 현재의 영어 수준이나 과거의 영어 학습 경험과 상관 없이 누구나 영어 논문 쓰기를 시작할 수 있는 방법에 관한 것입니다. 우선 영어에 대한 막연한 두려움, 과거 경험으로 인한 자신감 부족, 게다가 ‘쓰기’라는 쉽지 않은 인지 활동, 심지어 논문이라는 큰 벽, 혹은 섣부른 자신감 등 ‘영어 논문 쓰기’를 방해하는 요인을 생각해 봅니다. 이런 요인을 자세하게 들여다보면 각각의 걸림돌을 넘어갈 방법도 명쾌하게 발견할 수 있습니다. 이 세미나에서 다룰 구체적 내용은 다음과 같습니다.

한국인들이 ‘영어’와 ‘영어 쓰기’를 어렵게 느끼는 원인 이해하고 극복하기
‘읽기’와 ‘쓰기’의 서로 다른 두 가지 인지활동의 관련성 이해하고 적용하기
논문에 적합한 단어, 시제, 구두점 선택하고 사용하기
영문초록, 제목, 소제목 스타일 이해하고 작성하기
영어논문 쓰기를 돕는 디지털 도구 선택하고 활용하기

이 워크숍에서 안내될 몇 가지 방법은 영어 논문 쓰기가 더 이상 두려운 것이 아닌 연구자로서의 목표를 이루는 디딤돌이 되도록 할 것입니다.

Phase Estimation of Nonlinear State-space Model of the Circadian Pacemaker Using Level Set Kalman Filter and Raw Wearable Data
2022-06-10 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract:
Circadian rhythm is a robust internal 24 hours timekeeping mechanism maintained by the master circadian pacemaker Suprachiasmatic Nuclei (SCN). Numerous mathematical models have been proposed to capture SCN’s timekeeping mechanism and predict the circadian phase. There has been an increased demand for applying these models to the various unexplored data sets. One potential application is on data from commercially available wearable devices, which provide the noninvasive measurements of physiological proxies, such as activity and heart rate. Using these physiological proxies, we can estimate the circadian phase of the central and peripheral circadian pacemakers. Here, we propose a new framework for estimating the circadian phase using wearable data and the Level Set Kalman Filter on the nonlinear state-space model of the human circadian pacemaker. Analysis of over 200,000 days of wearable data from over 3,000 subjects using our framework successfully identified misalignment in central and peripheral pacemakers with a significantly smaller uncertainty than previous methods.

Dynamical System Perspective for Machine Learning
2022-06-13 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Abstract: The connection between deep neural networks and ordinary differential equations (ODEs) is an active field of research in machine learning. In this talk, we view the hidden states of a neural network as a continuous object governed by a dynamical system. The underlying vector field is written using a dictionary representation motivated by the equation discovery method. Within this framework, we develop models for two particular machine learning tasks: time-series classification and dimension reduction. We train the parameters in the models by minimizing a loss, which is defined using the solution to the governing ODE. To attain a regular vector field, we introduce a regularization term measuring the mean total kinetic energy of the flow, which is motivated by optimal transportation theory. We solve the optimization problem using a gradient-based method where the gradients are computed via the adjoint method from optimal control theory. Through various experiments on synthetic and real-world datasets, we demonstrate the performance of the proposed models. We also interpret the learned models by visualizing the phase plots of the underlying vector field and solution trajectories.

Optimized persistent random walk in zebrafish airineme search process
2022-06-15 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
In addition to diffusive signals, cells in tissue also communicate via long, thin cellular protrusions, such as airinemes in zebrafish. Before establishing communication, cellular protrusions must find their target cell. In this talk, we demonstrate that the shapes of airinemes in zebrafish are consistent with a persistent random walk model. The probability of contacting the target cell is maximized for a balance between ballistic search (straight) and diffusive search (highly curved, random). We find that the curvature of airinemes in zebrafish, extracted from live cell microscopy, is approximately the same value as the optimum in the simple persistent random walk model. We also explore the ability of the target cell to infer direction of the airineme’s source, finding that there is a theoretical trade-off between search optimality and directional information. This provides a framework to characterize the shape, and performance objectives, of non-canonical cellular protrusions in general.

Identifying the critical states of complex diseases by the dynamic change of multivariate distribution
2022-06-16 13:00 - 14:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Identifying the critical states of complex diseases by the dynamic change of multivariate distribution”, Peng, Hao, et al., Briefings in Bioinformatics, 2022.
Abstract: The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study, we propose a computational method, the direct interaction network-based divergence, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.

Deep Learning-based Uncertainty Quantification for Mathematical Models
2022-06-16 16:00 - 17:00 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
Over the recent years, various methods based on deep neural networks have been developed and utilized in a wide range of scientific fields. Deep neural networks are highly suitable for analyzing time series or spatial data with complicated dependence structures, making them particularly useful for environmental sciences and biosciences where such type of simulation model output and observations are prevalent. In this talk, I will introduce my recent efforts in utilizing various deep learning methods for statistical analysis of mathematical simulations and observational data in those areas, including surrogate modeling, parameter estimation, and long-term trend reconstruction. Various scientific application examples will also be discussed, including ocean diffusivity estimation, WRF-hydro calibration, AMOC reconstruction, and SIR calibration.

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
2022-06-23 12:30 - 13:30 | B378 Seminar room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, 34126, Korea, Republic of
We will discuss about “Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization”, Wang, Yingfan, et al., J. Mach. Learn. Res., 2021.
Abstract: Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMAP have demonstrated impressive visualization performance on many real world datasets. One tension that has always faced these methods is the trade-off between preservation of global structure and preservation of local structure: these methods can either handle one or the other, but not both. In this work, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is difficult to design a better method without a true understanding of the choices we make in our algorithms and their empirical impact on the lower-dimensional embeddings they produce. Towards the goal of local structure preservation, we provide several useful design principles for DR loss functions based on our new understanding of the mechanisms behind successful DR methods. Towards the goal of global structure preservation, our analysis illuminates that the choice of which components to preserve is important. We leverage these insights to design a new algorithm for DR, called Pairwise Controlled Manifold Approximation Projection (PaCMAP), which preserves both local and global structure. Our work provides several unexpected insights into what design choices both to make and avoid when constructing DR algorithms.
